Optimisation of Artefact Removal Algorithm for Microwave Imaging of the Axillary Region Using Experimental Prototype Signals

2021 ◽  
Author(s):  
Daniela M. Godinho ◽  
Joao M. Felicio ◽  
Carlos A. Fernandes ◽  
Raquel C. Conceicao
Keyword(s):  
Microwave Imaging ◽  
Experimental Prototype ◽  
Axillary Region ◽  
Artefact Removal

scholarly journals A Phantom Investigation to Quantify Huygens Principle Based Microwave Imaging for Bone Lesion Detection

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1505 ◽  
Author(s):  
Banafsheh Khalesi ◽  
Behnaz Sohani ◽  
Navid Ghavami ◽  
Mohammad Ghavami ◽  
Sandra Dudley ◽  
...  
Keyword(s):  
Bone Lesion ◽  
Microwave Imaging ◽  
Lesion Detection ◽  
Linear Scale ◽  
Frequency Range ◽  
Spatial Displacement ◽  
Artefact Removal ◽  
Huygens Principle ◽  
Transmitting Antenna ◽  
The Impact

This paper demonstrates the outcomes of a feasibility study of a microwave imaging procedure based on the Huygens principle for bone lesion detection. This study has been performed using a dedicated phantom and validated through measurements in the frequency range of 1–3 GHz using one receiving and one transmitting antenna in free space. Specifically, a multilayered bone phantom, which is comprised of cortical bone and bone marrow layers, was fabricated. The identification of the lesion’s presence in different bone layers was performed on images that were derived after processing through Huygens’ principle, the S21 signals measured inside an anechoic chamber in multi-bistatic fashion. The quantification of the obtained images was carried out by introducing parameters such as the resolution and signal-to-clutter ratio (SCR). The impact of different frequencies and bandwidths (in the 1–3 GHz range) in lesion detection was investigated. The findings showed that the frequency range of 1.5–2.5 GHz offered the best resolution (1.1 cm) and SCR (2.22 on a linear scale). Subtraction between S21 obtained using two slightly displaced transmitting positions was employed to remove the artefacts; the best artefact removal was obtained when the spatial displacement was approximately of the same magnitude as the dimension of the lesion.

scholarly journals Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5545
Author(s):  
Behnaz Sohani ◽  
James Puttock ◽  
Banafsheh Khalesi ◽  
Navid Ghavami ◽  
Mohammad Ghavami ◽  
...  
Keyword(s):  
Human Head ◽  
Haemorrhagic Stroke ◽  
Microwave Imaging ◽  
Ultra Wideband ◽  
Reference Image ◽  
Process Data ◽  
Imaging Device ◽  
Valid Comparison ◽  
Artefact Removal ◽  
Shaped Inclusion

In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An “ideal/reference” image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed.

scholarly journals Development of an Anthropomorphic Phantom of the Axillary Region for Microwave Imaging Assessment

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4968
Author(s):  
Matteo Savazzi ◽  
Soroush Abedi ◽  
Niko Ištuk ◽  
Nadine Joachimowicz ◽  
Hélène Roussel ◽  
...  
Keyword(s):  
Complex Permittivity ◽  
Current Knowledge ◽  
Conductive Polymer ◽  
Microwave Imaging ◽  
Good Representation ◽  
Axillary Lymph ◽  
Experimental Assessment ◽  
Imaging Technology ◽  
Axillary Region ◽  
3D Printed

We produced an anatomically and dielectrically realistic phantom of the axillary region to enable the experimental assessment of Axillary Lymph Node (ALN) imaging using microwave imaging technology. We segmented a thoracic Computed Tomography (CT) scan and created a computer-aided designed file containing the anatomical configuration of the axillary region. The phantom comprises five 3D-printed parts representing the main tissues of interest of the axillary region for the purpose of microwave imaging: fat, muscle, bone, ALNs, and lung. The phantom allows the experimental assessment of multiple anatomical configurations, by including ALNs of different size, shape, and number in several locations. Except for the bone mimicking organ, which is made of solid conductive polymer, we 3D-printed cavities to represent the fat, muscle, ALN, and lung and filled them with appropriate tissue-mimicking liquids. Existing studies about complex permittivity of ALNs have reported limitations. To address these, we measured the complex permittivity of both human and animal lymph nodes using the standard open-ended coaxial-probe technique, over the 0.5 GHz–8.5 GHz frequency band, thus extending current knowledge on dielectric properties of ALNs. Lastly, we numerically evaluated the effect of the polymer which constitutes the cavities of the phantom and compared it to the realistic axillary region. The results showed a maximum difference of 7 dB at 4 GHz in the electric field magnitude coupled to the tissues and a maximum of 10 dB difference in the ALN response. Our results showed that the phantom is a good representation of the axillary region and a viable tool for pre-clinical assessment of microwave imaging technology.

MICROWAVE IMAGING OF EMBEDDED OBJECT USING AN ENHANCED IMAGE RECONSTRUCTION ALGORITHM

2015 ◽  
Vol 74 (20) ◽  
pp. 1793-1801
Author(s):  
Sidi Mohammed Chouiti ◽  
Lotfi Merad ◽  
Sidi Mohammed Meriah ◽  
Xavier Raimundo
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Microwave Imaging ◽  
Image Reconstruction Algorithm

A Microwave Imaging System for the Detection of Targets Hidden behind Dielectric Walls

2020 ◽  
Author(s):  
Renato Cicchetti ◽  
Valentina Cicchetti ◽  
Sandra Costanzo ◽  
Paolo D'Atanasio ◽  
Alessandro Fedeli ◽  
...  
Keyword(s):  
Imaging System ◽  
Microwave Imaging

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Sign in / Sign up

Export Citation Format

Share Document